IVA-Based Spatio-Temporal Dynamic Connectivity Analysis in Large-Scale FMRI Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, much attention has been devoted to examining time-varying changes in functional connectivity to understand the network structure in the human brain. Most studies, however, analyze the time-varying functional connectivity but ignore the time-varying spatial information. In this paper, we propose a method based on independent vector analysis (IVA) to study dynamic functional network connectivity (dFNC) as well as dynamic spatial functional network connectivity (dsFNC) in fMRI data. Though IVA allows one to effectively capture both, its performance degrades with the increase in the number of datasets. Hence, we propose an effective scheme to bypass this limitation followed by graph theoretical analysis to study both inter-network dynamics and intra-network stationarity. We observe higher dFNC fluctuations for patients with schizophrenia in the default-mode (DM)-salience network and cerebellum with associated connections. dsFNC analysis indicates higher inter-network fluctuation in patients while DM, anterior DM and frontal networks demonstrate significant intra-network fluctuation in controls.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages965-969
Number of pages5
Volume2018-April
ISBN (Print)9781538646588
DOIs
Publication statusPublished - Sep 10 2018
Externally publishedYes
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: Apr 15 2018Apr 20 2018

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period4/15/184/20/18

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Keywords

  • Dynamic functional connectivity
  • Dynamic spatial connectivity
  • Independent vector analysis
  • Network stationarity
  • Temporal graphs

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Bhinge, S., Calhoun, V. D., & Adali, T. (2018). IVA-Based Spatio-Temporal Dynamic Connectivity Analysis in Large-Scale FMRI Data. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (Vol. 2018-April, pp. 965-969). [8461947] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8461947